Multi-objective evolutionary algorithms (MOEAs) have become
increasingly popular as multi-objective problem solving techniques.
An important open problem is to understand the role of populations in
MOEAs. We present two simple bi-objective problems which emphasise when
populations are needed. Rigorous runtime analysis points out an
exponential runtime gap between the population-based algorithm
Simple Evolutionary Multi-objective Optimiser (SEMO)
and several single individual-based algorithms on this problem. This
means that among the algorithms considered, only the population-based
MOEA is successful and all other algorithms fail.